A transfer learning-based approach for automated bone fracture classification in X-ray imaging.

ABSTRACT:

Background

Bone fractures present a significant diagnostic challenge in medical imaging, necessitating accurate and automated classification methods. Recent advancements in deep learning have greatly enhanced the diagnostic precision while reducing human error.

Objectives

This study proposes an ensemble deep learning model, EnsembleAttenBoneNet, that integrates fine-tuned ResNet50 and EfficientNetB3 models augmented with a Squeeze-and-Excitation (SE) attention mechanism, for robust classification of bone fractures in X-ray images.

Design

The dataset consists of ten distinct fracture categories, such as avulsion, comminuted, greenstick, and pathological fractures.

Methods

Preprocessing techniques, including resizing, normalization, and augmentation, have been ap

SUBMITTER: Bhuria R 

PROVIDER: S-EPMC12824133 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature